{"id":"W1985134713","doi":"10.1109/med.2010.5547730","title":"Support Vector Regression for soft sensor design of nonlinear processes","year":2010,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Soft sensor; Support vector machine; Computer science; Nonlinear system; Process (computing); Field (mathematics); Soft computing; Scale (ratio); Machine learning; Control engineering; Data mining; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009758365,0.00008005604,0.000130558,0.00003830648,0.00002230246,0.00001047567,0.00005855994,0.00007514666,0.0001166172],"category_scores_gemma":[0.0000930912,0.00005955166,0.00003365058,0.00007265264,0.00001044638,0.00004940198,0.000003450951,0.00006570924,0.00002164912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004749624,"about_ca_system_score_gemma":0.0000262312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005146405,"about_ca_topic_score_gemma":0.00002046284,"domain_scores_codex":[0.9995626,0.000005261943,0.0001615523,0.00008150423,0.00007574715,0.0001132926],"domain_scores_gemma":[0.9996493,0.00008443559,0.00002550338,0.0001129539,0.00008693256,0.00004091344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005431123,0.00002624158,0.00005413555,0.0004624665,0.00002664103,9.025669e-7,0.0001145246,0.002237219,0.9888001,0.00002922922,0.003380287,0.004813947],"study_design_scores_gemma":[0.0006994316,0.0001181588,0.00003032578,0.00002543577,0.00001235937,0.00001285946,0.00007789165,0.3550499,0.5906977,0.00001372768,0.05311059,0.0001515945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2511087,0.0001311666,0.7344666,0.0001962915,0.003772663,0.002008121,0.00006527322,0.001669178,0.006581985],"genre_scores_gemma":[0.9871061,0.000003720214,0.01063342,0.00001419647,0.0001833813,0.00005821703,0.000003543224,0.00002489512,0.001972552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7359974,"threshold_uncertainty_score":0.2428446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136743747013574,"score_gpt":0.2417260084398556,"score_spread":0.2280516337384982,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}